Note: The job is a remote job and is open to candidates in USA. Assail is seeking a Senior AI Engineer to join their Ares AI Engineering team, focusing on enhancing their autonomous offensive security platform for various applications. The role involves developing agents, training models, and improving system performance while ensuring customer-facing accuracy and production reliability.
Responsibilities
- Design, implement, and continuously improve the behavior and prompting of Ares' named agents, including orchestration patterns, hand-offs, planning loops, tool use, and shared memory
- Contribute to the model powering Ares across data curation, SFT, preference optimization (DPO/GRPO-style), and evaluation. Own pieces of the training pipeline from dataset construction through eval
- Extend the co-evolutionary self-training system that lets Ares learn from its own engagements and improve over time
- Build false-positive detection, tiered skill learning (suppression rules, agent directives, code-patch proposals), and the infrastructure that routes proposed changes through human approval and back into the platform
- Design rigorous, security-specific evaluations covering OWASP Top 10 coverage, exploit chaining, finding accuracy, and agent reliability. Track performance over every model and agent change
- Contribute to vision capabilities, mobile (iOS/Android) coverage, and BYOK support shipping in Sidewinder and beyond
- Own latency, cost, observability, and failure-mode analysis for agents running in customer engagements. Partner with the platform team on Kubernetes-based deployment
- Contribute to the live accuracy gauge and other surfaces where model and agent quality is exposed to customers
Skills
- 5+ years building production ML/AI systems, with at least 2 years working directly on LLMs or LLM-powered agents
- Deep Python; strong, production-grade engineering practices (testing, code review, observability)
- Hands-on fine-tuning experience: SFT, preference optimization (DPO, GRPO, RLHF/RLAIF), data curation, and synthetic data generation
- Strong grasp of transformer architectures and the modern training stack (PyTorch, Hugging Face, DeepSpeed or FSDP, accelerate)
- Experience designing and shipping multi-agent or tool-using LLM systems in production — not just demos
- Rigorous eval design: building harnesses, tracking experiments, and making model/agent decisions based on data rather than vibes
- Inference optimization experience: vLLM or TensorRT-LLM, quantization, throughput/latency tradeoffs
- Comfort with retrieval pipelines, vector stores, and structured memory for agents
- Kubernetes and containerized deployment fluency
- Genuine interest in offensive security and the ability to ramp quickly on OWASP Top 10, API security, web app pentesting, and mobile pentesting concepts. Direct offensive security background is a strong plus but not required
- Offensive security background: OSCP/OSWE/OSWA, CTF, bug bounty, or prior red team work
- Research publications at NeurIPS, ICML, ICLR, USENIX Security, IEEE S&P, Black Hat, or DEFCON
- Open source contributions to agent frameworks or LLM tooling
- Experience with adversarial ML or red-teaming AI systems
- Familiarity with mobile app reverse engineering or binary analysis
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